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Productivity and Scalability, ML Platform - Grid Dynamics


Boost the performance of the data science team

Data scientists’ time is expensive. Don’t waste it on non-differentiated work and avoid reinventing the wheel by creating machine learning platforms from scratch. A good ML platform will support the machine learning lifecycle from data ingestion to model serving and monitoring, increase the productivity of data analysts by 10x, support machine learning software and frameworks, enable automated machine learning, and let you scale the team more efficiently.


Increase the quality of ML decisions

The cost of errors in machine learning is getting higher as companies increasingly rely on closed-loop systems. Implementing model testing, data quality, model monitoring, and anomaly detection decreases the chances of production issues and facilitates high-quality insights.

MLOps, ML Platform - Grid Dynamics


Consistently deliver actionable insights

DevOps and Continuous Delivery became standard in application development long ago. But the core principles of DevOps can be expanded to the machine learning process within your business. With the right platform you can further increase efficiency with automated machine learning and by providing necessary machine learning algorithms and frameworks including deep learning and automl.


Deploy in the cloud

Using the cloud to enable new machine learning use cases is the simplest way to begin the cloud journey for data analytics. Migrate or deploy a new cloud platform to increase the agility and productivity of the data science team. Use it as a prototype for the larger cloud migration and let the data gravity shift to the cloud over time.


Make data-driven decisions at the edge

Some companies have significant infrastructure at the edge. Factories, stores, branches, distribution centers, gas stations, and a variety of IoT use cases may take advantage of deploying machine learning models locally to lower latency and make decisions without internet connectivity. These companies can take advantage of open source-based infrastructure agnostic data science platforms to make decisions in real-time at the edge.

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Starter Kits

How to choose and implement a machine learning platform?

A machine learning platform should support the end-to-end data science and machine learning lifecycle, facilitate collaboration between data analysts and data scientists, and enable the MLOps process. The main capabilities of the AI platform should include data ingestion, data preparation, and data exploration. It should also include feature selection, feature engineering, prototyping, experimentation, model training, validation, model testing, deployment to production, model serving, and monitoring.

A good platform should support a variety of machine learning algorithms including predictive analytics, deep learning, reinforcement learning, and the creation of various types of neural networks, etc. A data science and machine learning platform is typically an extension of an enterprise data analytics platform and should support a variety of integrations.

There are a variety of product vendors offering software as a service solutions. All major cloud providers have their own data science platform offerings. Good open source-based options exist too. Different options may work best for different companies, depending on their machine learning use cases, the maturity of the team, whether they are in the datacenter or in the cloud, and what cloud provider they’ve selected.

Our focus is on making the right choice for the right circumstances. We go beyond the deployment of the AI platform. We help you choose the right one, integrate it with the data lake or analytics platform, make the data available, onboard the MLOps process, train data scientists, implement a common library of machine learning models, and ensure that the data science process works smoothly from data to insights.

Choose ML Platform - Grid Dynamics


We have developed advanced artificial intelligence use cases, machine learning platforms, and onboard MLOps processes for Fortune-1000 enterprises across various industries including telecom, retail, media, gaming, and financial services.

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Technology and media

Most medium-sized technology and media companies have embraced the cloud and often use cloud-native platforms and require efficient integrations with analytical data platforms and advanced capabilities such as data quality, model validation, and monitoring. Larger companies with mature data science teams have greater flexibility with infrastructure-agnostic deployment and can avoid paying additional costs for the platform.

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Retail and brands

Retailers and brands have to move quickly to optimize the customer experience and back-office operations, including inventory and supply chain. For many of them, cloud services or 3rd party cloud agnostic machine learning platforms can be the best starting point. Retailers still planning the cloud migration journey can use the AI platform as a first step to move to the cloud and utilize it to implement advanced machine learning use cases. In some cases, cloud-native platforms can provide pre-built models and capabilities to further increase speed to insights.

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Finance and insurance

Security remains a major concern for banks for insurance companies. The largest ones may have challenges with the fractured big data ecosystem that was created over years of development and acquisitions. Depending on the state of the journey to the cloud and requirements for being infrastructure agnostic, different platforms may be a good fit, from cloud-native services to 3rd party products or open-source based platforms. The largest ones may find open-source machine learning platforms attractive due to their strong customization potential.

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Accelerate your journey to AI

We provide flexible engagement options to design and build ML platforms and artificial intelligence use cases, and onboard the MLOps process and culture. Contact us today to get started with a workshop, discovery, or PoC.

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